Can I do image classification with Multi Layers Perceptron (MLP)?
Multi Layers Perceptron(MLP) can be used for image classification, but it has a lot of deficiency than Convolutional Neural network(CNN). But if you compare MLP and Fisher Faces , the better one is MLP, because Fisher Faces will be increasingly difficult if adding more individuals or classes. You can make a simple MLP model, because it just has 3 layers which are an input layer, hidden layer and output layer, here a source code that you can try:


https://towardsdatascience.com/multi-layer-neural-networks-with-sigmoid-function-deep-learning-for-rookies-2-bf464f09eb7f


if you make a model, it will be based on training data, I think if make data trained composed based on noise in background and different light shadows on your image I think it will have a better performance, but remember if you are using MLP for image classification it can just predict an image on one spot, for example:
"if you train a model with the object in the middle of an image, your model can not predict it when the image is moved to the different spot".

here is pdf to see Fisher Faces performance:


https://core.ac.uk/download/pdf/84801048.pdf